from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 10 predictions (41.67%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
4	KNeighborsClassifier_brute_force	predict	0.067	100000	1000	100	3.109389	0.029569	NaN	0.000257	0.003109	brute	-1	5	0.743	0.210738	0.003562	0.676	14.754728	14.756835
5	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.027766	0.002686	NaN	0.000029	0.027766	brute	-1	5	1.000	0.008739	0.000301	0.000	3.177146	3.179025
7	KNeighborsClassifier_brute_force	predict	0.103	100000	1000	100	2.385740	0.005925	NaN	0.000335	0.002386	brute	1	100	0.846	0.211071	0.003652	0.743	11.303043	11.304735
13	KNeighborsClassifier_brute_force	predict	0.103	100000	1000	100	2.347518	0.010527	NaN	0.000341	0.002348	brute	1	5	0.743	0.261110	0.003644	0.846	8.990521	8.991397
16	KNeighborsClassifier_brute_force	predict	0.067	100000	1000	100	1.238940	0.007686	NaN	0.000646	0.001239	brute	1	1	0.676	0.213562	0.002368	0.743	5.801300	5.801656
17	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.021695	0.000568	NaN	0.000037	0.021695	brute	1	1	0.000	0.009116	0.000270	1.000	2.379967	2.381007
22	KNeighborsClassifier_brute_force	predict	0.038	100000	1000	2	3.031138	0.041008	NaN	0.000005	0.003031	brute	-1	5	0.883	0.032786	0.000553	0.845	92.452288	92.465459
25	KNeighborsClassifier_brute_force	predict	0.004	100000	1000	2	2.314031	0.003948	NaN	0.000007	0.002314	brute	1	100	0.887	0.035058	0.000922	0.883	66.006221	66.029022
31	KNeighborsClassifier_brute_force	predict	0.004	100000	1000	2	2.318878	0.004085	NaN	0.000007	0.002319	brute	1	5	0.883	0.077524	0.001569	0.887	29.911812	29.917934
34	KNeighborsClassifier_brute_force	predict	0.038	100000	1000	2	1.095901	0.002899	NaN	0.000015	0.001096	brute	1	1	0.845	0.035396	0.001246	0.883	30.960817	30.979992

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.448	0.0	-1	1	0.050	0.0	0.248	0.248	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.0	6.111	0.0	-1	5	0.049	0.0	0.266	0.266	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.0	6.258	0.0	1	100	0.050	0.0	0.255	0.255	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.0	6.289	0.0	-1	100	0.049	0.0	0.258	0.258	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.552	0.0	1	5	0.050	0.0	0.245	0.245	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.0	6.398	0.0	1	1	0.050	0.0	0.250	0.250	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.316	0.0	-1	1	0.010	0.0	0.513	0.513	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.319	0.0	-1	5	0.010	0.0	0.494	0.494	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.315	0.0	1	100	0.010	0.0	0.510	0.510	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.317	0.0	-1	100	0.010	0.0	0.509	0.509	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.317	0.0	1	5	0.010	0.0	0.502	0.502	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.315	0.0	1	1	0.010	0.0	0.510	0.510	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.015	0.056	0.000	0.002	-1	1	0.211	0.003	9.541	9.542	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.003	0.000	0.026	-1	1	0.009	0.000	2.966	2.967	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.109	0.030	0.000	0.003	-1	5	0.211	0.004	14.755	14.757	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.028	0.003	0.000	0.028	-1	5	0.009	0.000	3.177	3.179	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.386	0.006	0.000	0.002	1	100	0.211	0.004	11.303	11.305	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.000	0.000	0.023	1	100	0.010	0.000	2.371	2.372	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.136	0.052	0.000	0.003	-1	100	0.262	0.004	11.968	11.970	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.003	0.000	0.026	-1	100	0.009	0.001	2.885	2.890	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.348	0.011	0.000	0.002	1	5	0.261	0.004	8.991	8.991	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.001	0.000	0.023	1	5	0.009	0.001	2.515	2.519	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.239	0.008	0.001	0.001	1	1	0.214	0.002	5.801	5.802	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.001	0.000	0.022	1	1	0.009	0.000	2.380	2.381	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.782	0.028	0.000	0.002	-1	1	0.033	0.001	53.286	53.330	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.009	0.003	0.000	0.009	-1	1	0.001	0.000	11.257	11.370	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	3.031	0.041	0.000	0.003	-1	5	0.033	0.001	92.452	92.465	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.009	0.002	0.000	0.009	-1	5	0.001	0.000	10.557	10.637	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.314	0.004	0.000	0.002	1	100	0.035	0.001	66.006	66.029	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.000	0.000	0.004	1	100	0.001	0.000	4.128	4.262	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	3.026	0.031	0.000	0.003	-1	100	0.079	0.002	38.443	38.459	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.011	0.002	0.000	0.011	-1	100	0.001	0.000	10.139	10.357	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.319	0.004	0.000	0.002	1	5	0.078	0.002	29.912	29.918	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.000	0.000	0.004	1	5	0.001	0.000	3.726	3.751	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.096	0.003	0.000	0.001	1	1	0.035	0.001	30.961	30.980	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.045	2.088	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 8 predictions (33.33%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	1.727369	0.007845	NaN	0.000046	0.001727	kd_tree	1	5	0.975	0.104024	0.001276	0.964	16.605563	16.606811
4	KNeighborsClassifier_kd_tree	predict	0.002	1000000	1000	10	0.943520	0.016693	NaN	0.000085	0.000944	kd_tree	-1	5	0.975	0.533486	0.001715	0.973	1.768594	1.768603
10	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	0.805566	0.004904	NaN	0.000099	0.000806	kd_tree	1	1	0.964	0.182796	0.002145	0.975	4.406916	4.407219
13	KNeighborsClassifier_kd_tree	predict	0.002	1000000	1000	10	5.153384	0.020872	NaN	0.000016	0.005153	kd_tree	1	100	0.973	0.191664	0.007276	0.975	26.887598	26.906964
19	KNeighborsClassifier_kd_tree	predict	0.028	1000	1000	2	0.023010	0.000712	NaN	0.000695	0.000023	kd_tree	1	5	0.923	0.000775	0.000149	0.895	29.693353	30.237765
22	KNeighborsClassifier_kd_tree	predict	0.004	1000	1000	2	0.024607	0.000654	NaN	0.000650	0.000025	kd_tree	-1	5	0.923	0.005540	0.000724	0.919	4.441607	4.479377
28	KNeighborsClassifier_kd_tree	predict	0.028	1000	1000	2	0.021262	0.000404	NaN	0.000753	0.000021	kd_tree	1	1	0.895	0.001223	0.000229	0.923	17.384576	17.685817
31	KNeighborsClassifier_kd_tree	predict	0.004	1000	1000	2	0.037690	0.001014	NaN	0.000425	0.000038	kd_tree	1	100	0.919	0.000990	0.000315	0.923	38.061027	39.942815

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.607	0.016	0.022	0.0	1	5	0.782	0.011	4.609	4.610	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.406	0.019	0.018	0.0	-1	5	0.746	0.006	5.902	5.903	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.289	0.058	0.019	0.0	-1	1	0.767	0.010	5.590	5.590	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.773	0.062	0.021	0.0	1	1	0.728	0.006	5.180	5.181	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.802	0.030	0.021	0.0	1	100	0.773	0.006	4.920	4.920	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.791	0.064	0.021	0.0	-1	100	0.746	0.003	5.083	5.083	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	1	5	0.004	0.003	0.144	0.168	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	-1	5	0.004	0.003	0.163	0.219	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	-1	1	0.001	0.000	0.546	0.550	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.025	0.0	1	1	0.001	0.000	0.570	0.580	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	1	100	0.001	0.000	0.615	0.622	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	-1	100	0.001	0.000	0.573	0.578	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.727	0.008	0.000	0.002	1	5	0.104	0.001	16.606	16.607	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	5.958	6.392	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.944	0.017	0.000	0.001	-1	5	0.533	0.002	1.769	1.769	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.002	0.000	0.005	-1	5	0.001	0.000	7.944	8.406	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.494	0.004	0.000	0.000	-1	1	0.104	0.001	4.770	4.770	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	11.702	12.846	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.806	0.005	0.000	0.001	1	1	0.183	0.002	4.407	4.407	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	4.705	4.991	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.153	0.021	0.000	0.005	1	100	0.192	0.007	26.888	26.907	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.000	0.000	10.197	10.957	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.009	0.016	0.000	0.003	-1	100	0.538	0.002	5.593	5.593	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	8.462	8.804	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.001	0.001	0.000	1	5	0.001	0.000	29.693	30.238	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	5.455	6.507	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.025	0.001	0.001	0.000	-1	5	0.006	0.001	4.442	4.479	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.005	0.004	0.000	0.005	-1	5	0.000	0.000	37.619	44.946	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.024	0.001	0.001	0.000	-1	1	0.001	0.000	40.445	43.278	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	19.429	23.298	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.000	0.001	0.000	1	1	0.001	0.000	17.385	17.686	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.706	6.826	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.038	0.001	0.000	0.000	1	100	0.001	0.000	38.061	39.943	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.805	7.074	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.037	0.001	0.000	0.000	-1	100	0.006	0.001	6.677	6.801	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.001	0.000	0.002	-1	100	0.000	0.000	18.346	21.233	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.629	0.006	30	0.025	0.0	k-means++	0.473	0.021	1.330	1.331	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.545	0.004	30	0.029	0.0	random	0.431	0.013	1.264	1.264	See	See
6	KMeans_tall	fit	1000000	1000000	100	6.147	0.044	30	0.130	0.0	k-means++	2.898	0.010	2.121	2.121	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.812	0.024	30	0.138	0.0	random	2.742	0.018	2.120	2.120	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.0	30	0.010	0.000	k-means++	0.0	0.0	10.048	11.425	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.0	30	0.000	0.002	k-means++	0.0	0.0	11.899	13.687	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.0	30	0.010	0.000	random	0.0	0.0	9.245	10.353	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.0	30	0.000	0.002	random	0.0	0.0	11.972	14.021	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.0	30	0.370	0.000	k-means++	0.0	0.0	7.542	8.227	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.0	30	0.001	0.002	k-means++	0.0	0.0	11.518	13.293	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.0	30	0.412	0.000	random	0.0	0.0	6.521	7.143	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.0	30	0.001	0.002	random	0.0	0.0	10.219	11.391	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 3 predictions (37.5%).
The observed differences can be found in the diff_adjusted_rand_scores column.
The chosen difference threshold is 0.001 for adjusted_rand_score.
See details in the dataframe below.

	estimator	function	diff_adjusted_rand_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup
4	KMeans_short	predict	0.001361	10000	1000	2	0.002057	0.000244	20	0.007778	0.000002	full	k-means++	20	300	1	1.000000e-16	0.000744	0.000623	0.000110	-0.000617	3.303227	3.354649
7	KMeans_short	predict	0.002111	10000	1000	100	0.003093	0.000290	20	0.258674	0.000003	full	random	20	300	1	1.000000e-16	0.340206	0.001317	0.000245	0.342317	2.349128	2.389577
10	KMeans_short	predict	0.016992	10000	1000	100	0.002997	0.000426	20	0.266897	0.000003	full	k-means++	20	300	1	1.000000e-16	0.321348	0.001286	0.000305	0.338339	2.330841	2.395681

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.086	0.002	20	0.002	0.0	random	0.029	0.001	2.948	2.948	See	See
3	KMeans_short	fit	10000	10000	2	0.246	0.002	20	0.001	0.0	k-means++	0.095	0.001	2.603	2.604	See	See
6	KMeans_short	fit	10000	10000	100	0.222	0.002	20	0.036	0.0	random	0.122	0.002	1.816	1.816	See	See
9	KMeans_short	fit	10000	10000	100	0.663	0.010	20	0.012	0.0	k-means++	0.342	0.005	1.942	1.942	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	random	0.001	0.0	3.646	3.730	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	random	0.000	0.0	10.539	12.020	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	k-means++	0.001	0.0	3.303	3.355	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	10.447	11.889	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.259	0.000	random	0.001	0.0	2.349	2.390	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	random	0.000	0.0	9.129	10.116	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.267	0.000	k-means++	0.001	0.0	2.331	2.396	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	8.723	9.873	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.302	0.042	[20]	0.071	0.000	1.961	0.011	5.762	5.763	See	See
3	LogisticRegression	fit	1000	1000	10000	0.780	0.024	[27]	0.103	0.001	0.762	0.028	1.023	1.023	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.713	0.0	0.000	0.0	0.739	0.779	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.010	0.0	0.000	0.0	0.262	0.280	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[27]	4.422	0.0	0.003	0.0	0.556	0.558	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[27]	0.753	0.0	0.001	0.0	0.137	0.140	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.167	0.002	0.480	0.0	0.171	0.003	0.976	0.977	See	See
3	Ridge	fit	1000000	1000000	100	1.132	0.009	0.707	0.0	0.242	0.003	4.668	4.669	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.0	7.978	0.0	0.017	0.001	0.581	0.581	See	See
2	Ridge	predict	1000	1	10000	0.00	0.0	1.019	0.0	0.000	0.000	0.673	0.775	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.0	4.854	0.0	0.000	0.000	0.639	0.700	See	See
5	Ridge	predict	1000000	1	100	0.00	0.0	0.012	0.0	0.000	0.000	0.601	0.754	See	See